INTEGRATING SUPPLY CHAIN ANALYTICS TO IMPROVE DECISION MAKING

Abstract

Small and medium-sized enterprises (SMEs) play an important role in any economy, but are particularly susceptible to the risks associated with challenges, such as supply chain disruptions, out of stock situations and changing customer demands. These challenges highlight the need for digital tools to help make more data-driven and flexible decisions. The purpose of this study is to demonstrate how the integration of supply chain analytics can enhance the resilience and adaptability of SMEs to changes in the market environment. The project uses an integrated approach combining methods of decision evaluation (ABC-XYZ analysis), demand forecasting (ARIMA, ARIMAX, exponential smoothing), and planning (programming models, scenario analysis). Unlike static approaches, the proposed model takes into account seasonality, uncertainty, and changing conditions, providing more accurate inventory planning and management. The results show that the use of analytics allows SMEs to adapt to fluctuations in demand, reduce costs, reduce the risks of stockouts or excess of goods, and improve overall operational efficiency. The study contributes to the development of practical strategies to increase the sustainability and competitiveness of SMEs in an unstable environment.